Societal cost of traumatic brain injury: A comparison of cost-of-injuries related to biking with and without helmet use
Bibliographic record
Abstract
OBJECTIVE: The goal of this study is to determine if a difference in societal costs exists from traumatic brain injuries (TBI) in patients who wear helmets compared to non-wearers. METHODS: This is a retrospective cost-of-injury study of 128 patients admitted to the Montreal General Hospital (MGH) following a TBI that occurred while cycling between 2007-2011. Information was collected from Quebec Trauma Registry. The independent variables collected were socio-demographic, helmet status, clinical and neurological patient information. The dependent variables evaluated societal costs. RESULTS: The median costs of hospitalization were significantly higher (p = 0.037) in the no helmet group ($7246.67 vs. $4328.17). No differences in costs were found for inpatient rehabilitation (p = 0.525), outpatient rehabilitation (p = 0.192), loss of productivity (p = 0.108) or death (p = 1.000). Overall, the differences in total societal costs between the helmet and no helmet group were not significantly different (p = 0.065). However, the median total costs for patients with isolated TBI in the non-helmet group ($22, 232.82) was significantly higher (p = 0.045) compared to the helmet group ($13, 920.15). CONCLUSION: Cyclists sustaining TBIs who did not wear helmets in this study were found to cost society nearly double that of helmeted cyclists.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".